Atlas-based segmentation is a popular generic technique for automated delineation of structures in volumetric
data sets. Several studies have shown that multi-atlas based segmentation methods outperform schemes that use
only a single atlas, but running multiple registrations on large volumetric data is too time-consuming for routine
clinical use. We propose a generally applicable adaptive local multi-atlas segmentation method (ALMAS) that
locally decides how many and which atlases are needed to segment a target image. Only the selected parts of
atlases are registered. The method is iterative and automatically stops when no further improvement is expected.
ALMAS was applied to segmentation of the heart on chest CT scans and compared to three existing atlas-based
methods. It performed significantly better than single-atlas methods and as good as multi-atlas methods at a
much lower computational cost.